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Deterministic and Stochastic Modeling of Insulin Sensitivity

Diabetes mellitus is a common disease where a person has high blood glucose levels. The
disease has two main causes. The first one is inability of the pancreas to produce enough
insulin. The second one is the inability of cells to respond to the insulin produced by the
pancreas. In type 2 diabetes patients, the body fails to respond to insulin which results
in low insulin sensitivity". In this thesis, measurements from Intra Venous Glucose
Tolerance Test (IVGTT) for both healthy subjects and type 2 diabetes patients were
used together with Bergman's deterministic minimal model (ODE) to estimate the insulin
sensitivity based on a nonlinear mixed effect model. In addition to the IVGTT data some
basic covariates were included and tested for significance. Type 2 diabetes patients are
shown to be less sensitive to insulin than healthy subjects and thus need larger amount
of insulin to lower blood glucose level. A linear regression model from the covariates was
used for estimating insulin sensitivity but did not give conclusive results. The covariates
were included in the nonlinear mixed effect model to achieve better parameter estimates.
By incorporating the covariates the estimated standard deviation for insulin sensitivity
decreased substantially. An attempt was made to extend the deterministic minimal
model to a stochastic differential equation (SDE) model to improve the performance and
to get better parameter estimates.

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BibTeX @mastersthesis{Ösp Vilhjálmsdóttir2013,author={Ösp Vilhjálmsdóttir, Elín},title={Deterministic and Stochastic Modeling of Insulin Sensitivity},abstract={Diabetes mellitus is a common disease where a person has high blood glucose levels. The
disease has two main causes. The first one is inability of the pancreas to produce enough
insulin. The second one is the inability of cells to respond to the insulin produced by the
pancreas. In type 2 diabetes patients, the body fails to respond to insulin which results
in low insulin sensitivity". In this thesis, measurements from Intra Venous Glucose
Tolerance Test (IVGTT) for both healthy subjects and type 2 diabetes patients were
used together with Bergman's deterministic minimal model (ODE) to estimate the insulin
sensitivity based on a nonlinear mixed effect model. In addition to the IVGTT data some
basic covariates were included and tested for significance. Type 2 diabetes patients are
shown to be less sensitive to insulin than healthy subjects and thus need larger amount
of insulin to lower blood glucose level. A linear regression model from the covariates was
used for estimating insulin sensitivity but did not give conclusive results. The covariates
were included in the nonlinear mixed effect model to achieve better parameter estimates.
By incorporating the covariates the estimated standard deviation for insulin sensitivity
decreased substantially. An attempt was made to extend the deterministic minimal
model to a stochastic differential equation (SDE) model to improve the performance and
to get better parameter estimates.},publisher={Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers tekniska högskola},place={Göteborg},year={2013},note={65},}

RefWorks RT GenericSR ElectronicID 179041A1 Ösp Vilhjálmsdóttir, ElínT1 Deterministic and Stochastic Modeling of Insulin SensitivityYR 2013AB Diabetes mellitus is a common disease where a person has high blood glucose levels. The
disease has two main causes. The first one is inability of the pancreas to produce enough
insulin. The second one is the inability of cells to respond to the insulin produced by the
pancreas. In type 2 diabetes patients, the body fails to respond to insulin which results
in low insulin sensitivity". In this thesis, measurements from Intra Venous Glucose
Tolerance Test (IVGTT) for both healthy subjects and type 2 diabetes patients were
used together with Bergman's deterministic minimal model (ODE) to estimate the insulin
sensitivity based on a nonlinear mixed effect model. In addition to the IVGTT data some
basic covariates were included and tested for significance. Type 2 diabetes patients are
shown to be less sensitive to insulin than healthy subjects and thus need larger amount
of insulin to lower blood glucose level. A linear regression model from the covariates was
used for estimating insulin sensitivity but did not give conclusive results. The covariates
were included in the nonlinear mixed effect model to achieve better parameter estimates.
By incorporating the covariates the estimated standard deviation for insulin sensitivity
decreased substantially. An attempt was made to extend the deterministic minimal
model to a stochastic differential equation (SDE) model to improve the performance and
to get better parameter estimates.PB Institutionen för matematiska vetenskaper, matematisk statistik, Chalmers tekniska högskola,LA engLK http://publications.lib.chalmers.se/records/fulltext/179041/179041.pdfOL 30